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International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021

作者： Zhu, Ningbo Yang, Fei Zhu, Mingzhi Sun, Xinyao Cheng, Irene University of Alberta Computing Science Department Multimedia Program 2-32 Athabasca Hall University of Alberta EdmontonABT6G 2E8 Canada

ISBN: (纸本)9789898704320

The cryptocurrency industry has evolved rapidly in recent years, and it is increasingly popular as a convenient tool to complement the traditional stock and futures exchanges. Accurate market research enables traders to make more informed decisions and benefit from their investments. Our objective is to introduce a web platform for aggregating various types of cryptocurrency data, both on- and off-chain. Its novelty lies in offering a visual representation of market data analysis, which is driven by multi-modal data fusion and representation techniques, as well as artificial intelligence. We propose a full-stack framework that consists of a front-end web application for user interaction and visualization, and a backend server for data fetching, preprocessing, and analysis. In our implementation, we used data from the cryptocurrency market, on-chain statistics, and textual data from social media, to create a deep-learning-based market trend model. For market prediction, our data analysis module processed high-frequency vocabulary extracted from social media, sentiment analysis of social media content, historical price trend, and historical hash rates. Investors and market analysts can benefit from our platform by directly observing the dynamic of multi-modal cryptocurrency data and easily exploring market trends, generated by our market prediction model delivered by a front-end application. The complete implementation can be found in our publicly available GitHub link upon request. © 2021 International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： Sentiment analysis

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Modelling IF Steels Using Artificial Neural Networks and Automated Machine Learning 20th

Modelling IF Steels Using Artificial Neural Networks and Aut...

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20th International Conference on Hybrid Intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Sgrott, Douglas Macedo Cerqueira, Fabricio Moreira Miranda, Fabiano J. F. Filho, José F. S. Parpinelli, Rafael S. UDESC Graduate Program in Applied Computing Santa Catarina State University JoinvilleSC Brazil Global Research and Development Group ArcelorMittal São Francisco do SulSC Brazil

ISBN: (纸本)9783030730499

The development of steels requires multiples manufacturing steps and involves a high number of variables and non-linear processes. Although there are data-driven models used to predict such steels’ mechanical properties, their design often requires specialized Machine Learning knowledge, hampering the application of such models. In this paper, an Automated Machine Learning (AutoML) framework, namely Auto-Keras, was employed to generate and train a data-driven model on a steel data-set through Neural Architecture Search. This paper proposes predicting the mechanical properties of steels applying Artificial Neural Networks (ANN) as a multivariate regression problem using AutoML. The results show that even with a vast parameter search space, it is possible to generate ANNs with different architectures that provide high performance with free and open-source AutoML. Three ANNs with different activation functions were optimized using Auto-Keras and compared. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Real-time open field cattle monitoring by drone: A 3D visualization approach 15

Real-time open field cattle monitoring by drone: A 3D visual...

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作者： Yang, Fei Zhu, Ningbo Pei, Shuonan Cheng, Irene University of Alberta Computing Science Department Multimedia Program 2-32 Athabasca Hall University of Alberta EdmontonABT6G 2E8 Canada

ISBN: (纸本)9789898704320

Monitoring a large herd across an open field is challenging in the agricultural industry but is essential for the welfare of cattle. With the advancement of Unmanned Aerial Vehicle (UAV) technology, drones are now commonly used for surveillance. In this work, we apply UAV technology and drone-captured videos to monitor cattle in open pastures. We use cow headcount as a use case. Although cattle headcount in a confined indoor environment has been studied extensively, our contribution lies in developing a framework that can localize the cows in the video and track their movements on a 3D canvas in real-time. By using a 3D visualization approach, we expect to resolve many of the occlusion issues by guiding the drone operator to navigate the drone to discover important information. We use a pre-trained Mask R-CNN classification model to detect and track cows in the video. We then use Matplotlib 3D to create the 3D canvas and display the relative cow positions. Our real-time 3D cow visualization framework allows tracking herds remotely, saving time and labor for on-site manual herding, as well as providing better global monitoring of the herd. The complete implementation can be found in our publicly available GitHub link upon request. © 2021 International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-Conference on Computer Science and Information Systems, MCCSIS 2021. All rights reserved.

关键词： Object detection

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of Engineering & Technology 2024.

关键词： Genetic programming

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Enhancing Synthetic Well Logs with PCA-Based GAN Models

Enhancing Synthetic Well Logs with PCA-Based GAN Models

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International Conference on Machine Learning and Applications (ICMLA)

作者： Luciano Garim Garcia Gabriel De Oliveira Ramos José Manuel Marques Teixeira de Oliveira Ariane Santos Da Silveira Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos São Leopoldo Brazil Center of Excellence in Geological and Numerical Modeling Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

The generation of synthetic well log data is crucial for enhancing the understanding and exploration of subsurface reservoirs. This paper introduces a novel Generative Adversarial Network (GAN) model that incorporates a Principal Component Analysis (PCA)-based loss function to improve the quality of synthetic well log data. The proposed method is distinguished by its ability to generate complete well log datasets, rather than just individual logs or completing partial logs. Traditional GANs utilize cross-entropy loss but often fail to capture the complex structural patterns inherent in well logs. By integrating PCA into the loss function, our model not only distinguishes real from synthetic data but also ensures that the synthetic data retains the intrinsic variability and relationships observed in real logs. We validate our approach using histograms, correlation heatmaps, dimensionality reduction techniques (PCA and t-SNE), and a discriminative task. Results show that synthetic data generated with PCA-based loss aligns closely with real data, demonstrating superior preservation of statistical and structural characteristics. This advancement in synthetic data generation holds promise for enriching subsurface data analysis and exploration.

关键词： Training Analytical models Data analysis Correlation Scalability Generative adversarial networks Transformers Data models Synthetic data Principal component analysis

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Quantum Fourier Transform of Atrial Fibrillation

Quantum Fourier Transform of Atrial Fibrillation

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Quantum computing and Engineering (QCE), IEEE International Conference on

作者： Yu-Ching Chen Chih-Yu Chen Tsung-Wei Huang Chia-Ho Ou Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan Taiwan Quantum Information Center Chung Yuan Christian University Taoyuan Taiwan Department of Computer Science and Information Engineering National Pingtung University Pingtung Taiwan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Electrocardiograms (ECGs) are crucial for detecting cardiac diseases like atrial fibrillation (AF). Traditional analysis methods like fast Fourier transform (FFT) face challenges with increasing data complexity. This study uses Taipei Veterans General Hospital data to explore quantum Fourier transform (QFT) for ECG analysis. Results show that QFT effectively analyzes ECG signals, matching FFT performance while benefiting from quantum computing's efficiency.

关键词： Heart Quantum computing Fast Fourier transforms Hospitals High performance computing Atrial fibrillation Machine learning Electrocardiography Real-time systems Signal analysis

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The Influence of Feature Selection on Job Clustering for an E-recruitment Recommender System 19th

The Influence of Feature Selection on Job Clustering for an ...

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19th International Conference on Artificial Intelligence and Soft computing, ICAISC 2020

作者： Junior, Joel J. S. Vilasbôas, Fabricio G. de Castro, Leandro N. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University São Paulo Brazil

ISBN: (纸本)9783030615338

Recommender systems aim to effectively recommend items to the user based on their profile. An online recruitment system recommends jobs for a candidate according to his profile and can also act in reverse, recommending more qualified candidates for a particular job. Defining which variables will be used impacts directly the recommendation quality so that, when using the most important variables, we have a better assertiveness in the process. The goal of this work is to select the most important features of an online recruitment database using feature selection techniques. More specifically, we used the algorithms of Mitra, SUD and ACA to perform feature selection. The datasets used were derived from the original dataset assuming three distinct scenarios: the dataset containing the attributes related with the jobs' features;the dataset containing the bag of words of the description feature of the jobs;and the dataset resulting from the union of the two previous ones. The features' subsets selected in each of the above scenarios had their performance evaluated in a clustering task. The results obtained in each scenario show a performance gain of the clustering process when feature selection is made over the original data. Also, it was observed that the jobs' features result in better performance than the other two cases. © 2020, Springer Nature Switzerland AG.

关键词： Recommender systems

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancements in Quantum Radar Technology An Overview of Experimental Methods and Quantum Electrodynamics Considerations

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IEEE Nanotechnology Magazine 2024年第3期18卷 4-14页

作者： Lin, Yu-Cheng Huang, Tsung-Wei Tsai, Pin-Ju Chen, Yen-Hung Zhong, Yuan-Liang Chang, Ching-Ray National Taiwan University Department of Physics Taipei106319 Taiwan Chung-Yuan Christian University Quantum Information Center Taoyuan City320 Taiwan Chung-Yuan Christian University Undergraduate Program in Intelligent Computing and Big Data Taoyuan City320 Taiwan National Central University Department of Optics and Photonics Taoyuan City320 Taiwan National Central University Quantum Technology Center College of Science Taoyuan City320 Taiwan National Central University Center for Astronautical Physics and Engineering Taoyuan City320 Taiwan Chung-Yuan Christian University Department of Physics Taoyuan City320 Taiwan

This paper provides a brief introduction of the current state of quantum radar (QR) technology development, focusing on the experimental methods used for modifying QR in a laboratory setting. We delve into the foundational principles of quantum electrodynamics and consider interferometric aspects. Quantum radar systems, empowered by quantum measurements, extend their capabilities beyond conventional target detection and recognition, encompassing the detection and identification of RF stealth platforms and advanced weapons systems. Quantum technology is gaining paramount significance in various research domains, with the emergence of the concept of quantum radar, which leverages the quantum states of photons to extract information from distant targets. The mechanism involves dispatching photons, or photon clusters, toward the target, whereupon they are absorbed and subsequently re-emitted. The crucial measurement process can be executed in two distinct manners. One approach entails an interferometric measurement, often referred to as phase measurement, conducted on the photons, while the alternative method involves the straightforward quantification of returning photons. These methods are respectively known as Interferometric QR and Quantum Illumination (QI). In both approaches, one can opt to employ stationary quantum states of photons or harness entangled states. Extensive research has demonstrated that the use of entangled states yields the most significant resolution enhancement, achieving optimal results under ideal conditions. Quantum states offer a substantial advantage by virtue of their inherent correlations, referred to as quantum correlations, which augment both resolution and signal-to-noise ratio (SNR) in the radar system. This paper explores the intricacies of these advancements in quantum radar technology, shedding light on the underlying principles and experimental methodologies. © 2007-2011 IEEE.

关键词： Photons

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